The importance of the DRY principle (Don't Repeat Yourself) is that codifies the idea that every piece of knowledge in a system should have a singular representation. In the world of code that means we should have a single implementation. On the other hand, WET (Write Every Time) leads to multiple implementations. The performance implications of DRY versus WET become very clear when you consider their effects on a performance profile. In short, their effects are many.

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The importance of the DRY principle (Don't Repeat Yourself) is that it codifies the idea that every piece of knowledge in a system should have a singular representation. In other words, knowledge should be contained in a single implementation. The antithesis of DRY is WET (Write Every Time). Our code is WET when knowledge is codified in several different implementations. The performance implications of DRY versus WET become very clear when you consider their numerous effects on a performance profile.

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To see the first effect let's consider a feature of our system (say ''X'') that is a CPU bottleneck. Let's say ''X'' consumes 30% of the CPU. Now let's consider that feature ''X'' has 10 different implementations. On average, each implementation will consume 3% of the CPU. Hardly a level of CPU utilization worth considering if we are looking for a big gain. In this scenario it is unlikely that we'd recognize that feature as being a bottleneck. That said, let's move to the second point by saying, magic has happened and we recognize feature ''X'' as the source of our problem. Now we are left with the problem of finding, recognizing, and fixing every single implementation. In our example we have 10 different implementations that we need to find and fix and all because we didn't follow the DRY principle. Following DRY we'd clearly see the 30% CPU utilization and we'd have 1/10 the code to fix, not to mention the time saved by not having to find each implementation.

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Let's start by considering a feature of our system, say ''X'', that is a CPU bottleneck. Let's say feature ''X'' consumes 30% of the CPU. Now let's say that feature ''X'' has ten different implementations. On average, each implementation will consume 3% of the CPU. As this level of CPU utilization isn't worth worrying about if we are looking for a quick win, it is likely that we'd miss that this feature is our bottleneck. However, let's say that we somehow recognized feature ''X'' as a bottleneck. We are now left with the problem of finding and fixing every single implementation. With WET we have ten different implementations that we need to find and fix. With DRY we'd clearly see the 30% CPU utilization and we'd have a tenth of the code to fix. And did I mention that we don't have to spend time hunting down each implementation?

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There is one use case where we are often guilty of violating DRY. That is in our use of collections. Let's say we are working with customer data. A common technique to implement a query would be to iterate over the collection and then apply the query in turn to each element.

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There is one use case where we are often guilty of violating DRY: our use of collections. A common technique to implement a query would be to iterate over the collection and then apply the query in turn to each element:

By exposing this raw collection to clients, we have violated encapsulation. This limits our ability to refactor and we have forced our clients to violate DRY by having each of them implement potentially the same query. One solution is to not expose raw collections in any API. In this example we can introduce a new collection called <code>CustomerList</code>. This new class is more semantically in line with our domain. It will act as a natural home for all our queries.

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By exposing this raw collection to clients, we have violated encapsulation. This not only limits our ability to refactor, it forces users of our code to violate DRY by having each of them re-implement potentially the same query. This situation can easily be avoided by removing the exposed raw collections from the API. In this example we can introduce a new, domain-specific collective type called <code>CustomerList</code>. This new class is more semantically in line with our domain. It will act as a natural home for all our queries.

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Having this new collection type will also allows to easily see if these queries are a performance bottleneck. By incorporating the queries into the class we eliminate the need to expose internal representations to our clients. This gives us the freedom to alter these implementations without fear of violating client contracts.

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Having this new collection type will also allow us to easily see if these queries are a performance bottleneck. By incorporating the queries into the class we eliminate the need to expose representation choices, such as <code>ArrayList</code>, to our clients. This gives us the freedom to alter these implementations without fear of violating client contracts:

In this example, adherence to DRY allowed us to introduced an alternate indexing scheme with <code>SortedList</code> keyed on our customers level of spending. More important than the specific details of this particular example that following DRY helped us to find and repair a performance bottleneck that would have been more difficult to find were the code to be WET.

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In this example, adherence to DRY allowed us to introduce an alternate indexing scheme with <code>SortedList</code> keyed on our customers level of spending. More important than the specific details of this particular example, following DRY helped us to find and repair a performance bottleneck that would have been more difficult to find were the code to be WET.

Current revision

The importance of the DRY principle (Don't Repeat Yourself) is that it codifies the idea that every piece of knowledge in a system should have a singular representation. In other words, knowledge should be contained in a single implementation. The antithesis of DRY is WET (Write Every Time). Our code is WET when knowledge is codified in several different implementations. The performance implications of DRY versus WET become very clear when you consider their numerous effects on a performance profile.

Let's start by considering a feature of our system, say X, that is a CPU bottleneck. Let's say feature X consumes 30% of the CPU. Now let's say that feature X has ten different implementations. On average, each implementation will consume 3% of the CPU. As this level of CPU utilization isn't worth worrying about if we are looking for a quick win, it is likely that we'd miss that this feature is our bottleneck. However, let's say that we somehow recognized feature X as a bottleneck. We are now left with the problem of finding and fixing every single implementation. With WET we have ten different implementations that we need to find and fix. With DRY we'd clearly see the 30% CPU utilization and we'd have a tenth of the code to fix. And did I mention that we don't have to spend time hunting down each implementation?

There is one use case where we are often guilty of violating DRY: our use of collections. A common technique to implement a query would be to iterate over the collection and then apply the query in turn to each element:

By exposing this raw collection to clients, we have violated encapsulation. This not only limits our ability to refactor, it forces users of our code to violate DRY by having each of them re-implement potentially the same query. This situation can easily be avoided by removing the exposed raw collections from the API. In this example we can introduce a new, domain-specific collective type called CustomerList. This new class is more semantically in line with our domain. It will act as a natural home for all our queries.

Having this new collection type will also allow us to easily see if these queries are a performance bottleneck. By incorporating the queries into the class we eliminate the need to expose representation choices, such as ArrayList, to our clients. This gives us the freedom to alter these implementations without fear of violating client contracts:

In this example, adherence to DRY allowed us to introduce an alternate indexing scheme with SortedList keyed on our customers level of spending. More important than the specific details of this particular example, following DRY helped us to find and repair a performance bottleneck that would have been more difficult to find were the code to be WET.